PSI - Issue 41

Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2022) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000–000 Available online at www.sciencedirect.com ScienceDirect

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Procedia Structural Integrity 41 (2022) 372–383

© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the MedFract2Guest Editors. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the MedFract2Guest Editors. Keywords: Structural Reliability; Crack Initiation and Growth; Stress Intensity Factor; Monte Carlo Simulation; Probabilistic Fracture Mechanics; Artificial Neural Networks Abstract To analyze the effect of intergranular stress corrosion cracking (IG-SCC) on the probability of boiling water reactor (BWR) pipe failure, we have introduced ome modifications to the IG-SCC m del for the piping reliability analysis, including seismic vents (PRAISE) code of probabilistic fracture mechanics. The purpose of this article is to evaluate the probability of failure under IG SCC of several pipe sizes using Monte Carlo simulations (MCS), s nsitivity analysis nd artificial neural networks (ANN). The MCS generates the reliability data nd input parameters for modeling and ANN training. Th ANN inputs are the sampled par meters, while the ANN outputs re the reliability estimated by the MCS. The entire database generated by the MCS will b separated into three groups. The data groups are intended for training, testing and validation ANN respectively. The percentage of the entire database for ach group shoul be determined according to specifi requirements. Examples are given to de onstrate the roposed method. The retained ANN can b used to efficiently and accurately stimate the reliability of leaks from damaged pipes. Finally, we have observed a strong correlation between the end of life failure prob bility and the parameter ch ract rizing the damage for each pipe size witch is predicted using a second ANN. This damage parameter can be used to evaluate structural reliability and identify the most effective approaches to improve pipe reliability. © 2022 The Authors. Published by ELSEVIER B.V. This is an open acces article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the MedFract2Guest Editors. Keywords: Structural Reliability; Crack Initiation and Growth; Stress Intensity Factor; Monte Carlo Simulation; Probabilistic Fracture Mechanics; Artificial Neural Networks Abstract To analyze the effect of intergranular stress corrosion cracking (IG-SCC) on the probability of boiling water reactor (BWR) pipe failure, we have introduced some modifications to the IG-SCC model for the piping reliability analysis, including seismic events (PRAISE) code of probabilistic fracture mechanics. The purpose of this article is to evaluate the probability of failure under IG SCC of several pipe sizes using Monte Carlo simulations (MCS), sensitivity analysis and artificial neural networks (ANN). The MCS generates the reliability data and input parameters for modeling and ANN training. The ANN inputs are the sampled parameters, while the ANN outputs are the reliability estimated by the MCS. The entire database generated by the MCS will be separated into three groups. The data groups are intended for training, testing and validation ANN respectively. The percentage of the entire database for each group should be determined according to specific requirements. Examples are given to demonstrate the proposed method. The retained ANN can be used to efficiently and accurately estimate the reliability of leaks from damaged pipes. Finally, we have observed a strong correlation between the end of life failure probability and the parameter characterizing the damage for each pipe size witch is predicted using a second ANN. This damage parameter can be used to evaluate structural reliability and identify the most effective approaches to improve pipe reliability. 2nd Mediterranean Conference on Fracture and Structural Integrity Piping reliability prediction using Monte Carlo simulation and artificial neural network Mohamed Amine Belyamna a , Chouaib Zeghida a , Samira Tlili b , Abdelmoumene Guedri a* 2nd Mediterranean Conference on Fracture and Structural Integrity Piping reliability prediction using Monte Carlo simulation and artificial neural network Mohamed Amine Belyamna a , Chouaib Zeghida a , Samira Tlili b , Abdelmoumene Guedri a* a Infra-Res Laboratory, Department of Mechanical Engineering, University of Souk Ahras, Souk Ahras, Algeria b Research Center in Industrial Technologies CRTI, P.O. Box 64, Cheraga 16014 Algiers, Algeria a Infra-Res Laboratory, Department of Mechanical Engineering, University of Souk Ahras, Souk Ahras, Algeria b Research Center in Industrial Technologies CRTI, P.O. Box 64, Cheraga 16014 Algiers, Algeria

* Corresponding author. Tel.: +213672831024. E-mail address: a.guedri@univ-soukahras.dz * Corresponding author. Tel.: +213672831024. E-mail address: a.guedri@univ-soukahras.dz

2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review under responsibility of the MedFract2Guest Editors. 2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an ope access article under t e CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review under responsibility of the MedFract2Guest Editors.

2452-3216 © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the MedFract2Guest Editors. 10.1016/j.prostr.2022.05.043